--- title: "Global Digi-Health Monitor: Calendar Week 21, 2025" language: "en" type: "post" original_url: "https://www.atlas-digitale-gesundheitswirtschaft.de/en/blog/2025/05/28/global-digi-health-monitor-calendar-week-21-2025/" human_version: "../../../../../../mensch/en/blog/2025/05/28/global-digi-health-monitor-calendar-week-21-2025/" date: "2025-05-28" section: "Monitor" categories: ["Monitor"] reading_time: "3 Min." description: "A comprehensive Swiss study of 1.933 physicians has exposed significant shortcomings in electronic medical record systems, with 56% of respondents reporting" publisher: "Lehrstuhl für Management und Innovation im Gesundheitswesen, Universität Witten/Herdecke" --- # Global Digi-Health Monitor: Calendar Week 21, 2025 *May 28, 2025 · Monitor* ## **National Survey Reveals Alarming Deficiencies in Electronic Medical Record Usability and Safety** A comprehensive Swiss study of 1.933 physicians has exposed significant shortcomings in electronic medical record systems, with 56% of respondents reporting their EMR fails to enhance patient safety and 50% finding it inefficient. Published in npj Digital Medicine, the research reveals EMRs achieved only 52% of maximum possible usability scores, with hospital implementations performing significantly worse than outpatient systems. Multilevel analysis demonstrated that 38% of usability variance stems from EMR vendor differences, while a striking 51% relates to hospital-specific implementations. Key discriminating features between EMR systems included response times, alert management, error prevention capabilities, and collaboration support. These findings contradict the promised benefits of healthcare digitalization and suggest minimal usability improvement over the past 15 years. The study utilized a novel System Usability and Risk Evaluation (SURE) instrument, which proved highly effective at identifying specific EMR strengths and weaknesses across healthcare settings. [Read More…](https://www.nature.com/articles/s41746-025-01657-4) --- ## **AI in Radiology: Ally, Not Adversary at Mayo Clinic** Step into the cutting-edge world of radiology at the Mayo Clinic, where AI is transforming medicine without stealing jobs, as reported on May 14, 2025. Contrary to predictions like Geoffrey Hinton’s 2016 forecast that AI would replace radiologists within five years, the technology has become a powerful ally in Rochester, Minn. At Mayo, over 250 AI models enhance workflows—sharpening images, automating tasks, detecting abnormalities like blood clots, and even predicting diseases such as pancreatic cancer years early. Tools developed with radiologists like Dr. Theodora Potretzke save 15-30 minutes per kidney scan, boosting efficiency with precise measurements. Far from displacing doctors, AI acts as a second set of eyes, amplifying human expertise while radiologists continue to interpret, advise, and connect with patients. With a 55% staff increase since 2016 and a dedicated AI team of 40, Mayo proves that humans and algorithms together redefine healthcare’s future. [Read More…](https://www.nytimes.com/2025/05/14/technology/ai-jobs-radiologists-mayo-clinic.html) --- ## **AI-Powered Cardiac Digital Twins: 3,800 Virtual Hearts Reveal New Disease Insights** Researchers have created over 3,800 personalized cardiac digital twins in a breakthrough study that drastically scales up this promising technology for precision medicine. Scientists from King’s College London, Imperial College London, and the Alan Turing Institute developed an AI-accelerated workflow that transforms MRI and ECG data into anatomically accurate heart simulations at unprecedented speed. Published in Nature Cardiovascular Research, the study revealed that age and obesity alter cardiac electrical properties—potentially explaining their association with heart disease—while anatomical differences between male and female hearts primarily stem from size variations rather than electrical conductivity. “By replicating hearts across the population, digital twins offer deeper insights into people at risk of heart disease,” explains senior author Steven Niederer. The researchers validated their models in 359 patients with ischemic heart disease and now plan to link heart function to genetics, potentially identifying novel biomarkers and therapeutic targets while enabling personalized prediction of treatment outcomes. [Read More…](https://www.insideprecisionmedicine.com/topics/precision-medicine/first-large-scale-cardiac-digital-twin-advances-precision-potential/) --- ## **Telemedicine Disparities Persist: Urban-Rural Gap and Payment Policies Shape Post-Pandemic Healthcare Landscape** A comprehensive analysis of 115.8 million outpatient consultations across 498 U.S. health systems reveals significant disparities in telemedicine adoption between 2019-2023. While telemedicine surged from <0.05% pre-pandemic to 25% in April 2020, settling at 4% by March 2023 (80× higher than pre-pandemic levels), urban facilities consistently utilized this modality 2.4× more than rural counterparts. Mental health services dominated telemedicine, comprising 29% of all such visits by 2023, followed by substance use disorders at 21%. State payment parity mandates—requiring equal reimbursement for virtual and in-person care—were associated with a 2.5 percentage point increase in telemedicine utilization, with particularly strong effects among smaller health systems and those serving more racial/ethnic minority patients. However, these policies didn’t bridge the urban-rural divide, suggesting that while telemedicine represents a lasting transformation in healthcare delivery, additional interventions beyond payment reform are needed to ensure equitable access across geographic and demographic boundaries. [Read More…](https://www.nature.com/articles/s43856-025-00757-2) --- ## **AI Model Predicts Lung Cancer Risk with Single CT Scan** A deep learning model named Sybil, developed by MIT and Harvard Medical School researchers, can accurately predict lung cancer risk using just one low-dose CT scan. In a Korean study involving over 21,000 individuals (including approximately 11,000 non-smokers), Sybil demonstrated impressive predictive accuracy—identifying future lung cancer development within one year with 86% accuracy and within six years with 74% accuracy. The model performed similarly well for non-smokers, which is particularly valuable as lung cancer rates rise among this demographic in Asia. Lead investigator Dr. Yeon Wook Kim from Seoul National University Bundang Hospital believes Sybil could significantly improve screening efficiency by identifying both truly low-risk individuals who can discontinue screening and high-risk groups requiring continued monitoring. [Read More…](https://www.insideprecisionmedicine.com/topics/oncology/lung-cancer-risk-accurately-predicted-by-ai-model/) --- ![• : Ipsen and Foreseen Biotechnology use AI in drug development • : Breakthrough by University of Cambridge researcher Liz Lee • : Telecare open hybrid telehealth clinic with GP’s Dr. Ken Tze Koh Australia and Raymond Wen • : Study by john Xuefeng jiang shows growing adoption rates] **Categories:** Monitor ## Images on this page - ATLAS_Logo_RGB_RZ.jpg: ../../../../../../mensch/assets/bilder/wp-content/uploads/2025/08/ATLAS_Logo_RGB_RZ.jpg --- This page is optimised for AI systems. Human-readable version: [Global Digi-Health Monitor: Calendar Week 21, 2025](../../../../../../mensch/en/blog/2025/05/28/global-digi-health-monitor-calendar-week-21-2025/)